Classification of HER2 Receptor Status in Breast Cancer Tissues by MALDI Imaging Mass Spectrometry

Rauser, Sandra; Marquardt, C.; Balluff, Benjamin; Deininger, Sören‐Oliver; Albers, Christian; Belau, Eckhard; Hartmer, Ralf; Suckau, Detlev; Specht, Katja; Ebert, Matthias; Schmitt, Manfred; Aubele, Michaela; Höfler, Heinz; Walch, Axel

doi:10.1021/pr901008d

Cited by 245 publications

(249 citation statements)

References 36 publications

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“…6,8,18 Whole animals sections have been imaged as part of preclinical drug evaluations. 14 Data are typically processed and evaluated with standard MSI analysis software, such as BIOMAP 19 , ClinProTools 20 or other such packages.…”

Section: Introductionmentioning

confidence: 99%

Robust Data Processing and Normalization Strategy for MALDI Mass Spectrometric Imaging

et al. 2012

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Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) provides localized information about the molecular content of a tissue sample. To derive reliable conclusions about MSI data, it is necessary to implement appropriate processing steps in order to compare peak intensities across the different pixels comprising the image. Here, we review commonly used normalization methods, and propose a rational data processing strategy, for robust evaluation and modeling of MSI data. The approach includes newly developed heuristic methods for selecting biologically relevant peaks and pixels to reduce the size of a data set and remove the influence of the applied MALDI matrix. The methods are demonstrated on a MALDI MSI data set of a sagittal section of rat brain (4750 bins, m/z = 50-1000, 111 × 185 pixels) and the proposed preferred normalization method uses the median intensity of selected peaks, which were determined to be independent of the MALDI matrix. This was found to effectively compensate for a range of known limitations associated with the MALDI process and irregularities in MS image sampling routines. This new approach is relevant for processing of all MALDI MSI data sets, and thus likely to have impact in biomarker profiling, preclinical drug distribution studies, and studies addressing underlying molecular mechanisms of tissue pathology.

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Section: Introductionmentioning

confidence: 99%

Robust Data Processing and Normalization Strategy for MALDI Mass Spectrometric Imaging

et al. 2012

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“…[4][5][6][7] This task requires measuring, processing, and understanding large numbers of patient samples to compare cohorts at several time points (see the review by McDonnell et al). 15 At present, the most common way of examining MALDI-imaging data set is the manual inspection of a mean spectrum of the data set, selection of large peaks, and visual examination of molecular images corresponding to the selected m/z-values.…”

Section: Introductionmentioning

confidence: 99%

Spatial Segmentation of Imaging Mass Spectrometry Data with Edge-Preserving Image Denoising and Clustering

et al. 2010

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In recent years, matrix-assisted laser desorption/ionization (MALDI)-imaging mass spectrometry has become a mature technology, allowing for reproducible high-resolution measurements to localize proteins and smaller molecules. However, despite this impressive technological advance, only a few papers have been published concerned with computational methods for MALDI-imaging data. We address this issue proposing a new procedure for spatial segmentation of MALDI-imaging data sets. This procedure clusters all spectra into different groups based on their similarity. This partition is represented by a segmentation map, which helps to understand the spatial structure of the sample. The core of our segmentation procedure is the edge-preserving denoising of images corresponding to specific masses that reduces pixel-to-pixel variability and improves the segmentation map significantly. Moreover, before applying denoising, we reduce the data set selecting peaks appearing in at least 1% of spectra. High dimensional discriminant clustering completes the procedure. We analyzed two data sets using the proposed pipeline. First, for a rat brain coronal section the calculated segmentation maps highlight the anatomical and functional structure of the brain. Second, a section of a neuroendocrine tumor invading the small intestine was interpreted where the tumor area was discriminated and functionally similar regions were indicated.

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“…In addition to gene expression profiling studies [9][10][11][12], various published proteomic studies have been published that have investigated the proteomic profile of either HER2-positive mammary tissues [13][14][15][16][17][18][19] or HER2-expressing cell lines [20][21][22][23][24][25][26]. Here, we report a quantitative proteomics investigation of a Her2-overexpressing epithelial cell line (H6O5), isolated from a primary mammary tumor of MMTV-Her2/neu transgenic mice.…”

Section: Introductionmentioning

confidence: 99%

Proteomic characterization of Her2/neu‐overexpressing breast cancer cells

Chen¹,

Pimienta²,

Gu³

et al. 2011

Proteomics Clinical Apps

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The receptor tyrosine kinase HER2 is an oncogene amplified in invasive breast cancer and its overexpression in mammary epithelial cell lines is a strong determinant of a tumorigenic phenotype. Accordingly, HER2-overexpressing mammary tumors are commonly indicative of a poor prognosis in patients. Several quantitative proteomic studies have employed two-dimensional gel electrophoresis in combination with tandem mass spectrometry, which provides only limited information about the molecular mechanisms underlying HER2/neu signaling. In the present study, we used a SILAC-based approach to compare the proteomic profile of normal breast epithelial cells with that of Her2/neu-overexpressing mammary epithelial cells, isolated from primary mammary tumors arising in MMTV-Her2/neu transgenic mice. We identified 23 proteins with relevant annotated functions in breast cancer, showing a substantial differential expression. This included overexpression of creatine kinase, retinol-binding protein 1, thymosin beta 4 and tumor protein D52, which correlated with the tumorigenic phenotype of Her2-overexpressing cells. The differential expression pattern of two genes, gelsolin and retinol binding protein 1, was further validated in normal and tumor tissues. Finally, an in silico analysis of published cancer microarray datasets revealed a 23-gene signature which can be used to predict the probability of metastasis-free survival in breast cancer patients.

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Classification of HER2 Receptor Status in Breast Cancer Tissues by MALDI Imaging Mass Spectrometry

Cited by 245 publications

References 36 publications

Robust Data Processing and Normalization Strategy for MALDI Mass Spectrometric Imaging

Robust Data Processing and Normalization Strategy for MALDI Mass Spectrometric Imaging

Spatial Segmentation of Imaging Mass Spectrometry Data with Edge-Preserving Image Denoising and Clustering

Proteomic characterization of Her2/neu‐overexpressing breast cancer cells

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